mct-blog-sonified.ipynb

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   "metadata": {},
   "source": [
    "MCT Blog Sonified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38cfc8c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import markdown\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "import librosa\n",
    "import IPython.display as ipd\n",
    "import scipy\n",
    "import soundfile as sf\n",
    "from collections import Counter\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "359dd153",
   "metadata": {},
   "outputs": [],
   "source": [
    "def md_to_text(md):\n",
    "    # function cleans markdown\n",
    "    html = markdown.markdown(md)\n",
    "    soup = BeautifulSoup(html, features='html.parser')\n",
    "    return soup.get_text()\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(\"(\\[.*\\])\", \"\", text)\n",
    "    text = re.sub(\"(http[s]?\\://\\S+)\", \"\", text)\n",
    "    text = re.sub(\"[0-9]\", \"\", text)\n",
    "    text = re.sub(\"\\S*@\\S*\\s?\", \"\", text)\n",
    "    text = re.sub(\"/\\S+/\\S+\", \"\", text)\n",
    "    text = re.sub(\"\\n\", \" \", text)\n",
    "    text = re.sub(r\"[^\\w\\s]\", \"\", text)\n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    text = re.sub(\"\\s+\", \" \", text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32834410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2018-08-22-introduction-group-b.md\n",
      "1 2018-08-22-introduction-group-c.md\n",
      "2 2018-08-23-introduction-group-a.md\n",
      "3 2018-08-23-introduction-group-d.md\n",
      "4 2018-09-07-opening-ceremony-stillness-under-tension.md\n",
      "5 2018-09-10-A-team-first-week.md\n",
      "6 2018-09-17-Group-B-setting-up-tico.md\n",
      "7 2018-10-03-A-team-presentation.md\n",
      "8 2018-10-16-A-Team-Recording and sampling.md\n",
      "9 2018-10-17-C-First-Day-What-Ever-Comes-Along.md\n",
      "10 2018-10-17-Group-B-Cicruit-sniffing-and-sampling.md\n",
      "11 2018-10-17-Group-B-Physical-computing-day-2.md\n",
      "12 2018-10-17-Group-C-Developing-Mobile-Musical-Instruments.md\n",
      "13 2018-10-18-A-team-Arduino and littleBits.md\n",
      "14 2018-10-18-A-team-Phone sensors, synths and samples.md\n",
      "15 2018-10-18-Group-B-christmas-already.md\n",
      "16 2018-10-18-Group-C-Think-Outside-The-Box.md\n",
      "17 2018-10-21-Group-D-Jamming-in-the-Portal.md\n",
      "18 2018-10-22-A-team-Workshop finale.md\n",
      "19 2018-10-22-Group-B-Making-the-Percampler.md\n",
      "20 2018-10-22-Group-C-The-Koolboard.md\n",
      "21 2018-10-29-Group-C-A-two-week-wrap-up.md\n",
      "22 2018-11-06-Group-B-short-cables-and-lola.md\n",
      "23 2018-11-08-Team A - Week 44.md\n",
      "24 2018-11-12-Exhibition-Metaverk-in-Trondheim.md\n",
      "25 2018-11-14-MCT-Heroes.md\n",
      "26 2018-11-18-Team-A-DSP-workshop.md\n",
      "27 2018-11-22-GroupB-Multichannel-LoLa.md\n",
      "28 2018-11-23-DSP-Workshop-Group-C.md\n",
      "29 2018-11-23-Group-B-DSP-workshop.md\n",
      "30 2018-11-28-Mutual-Concert-Between-Oslo-and-Trondheim-Personal-Reflections.md\n",
      "31 2018-11-29-Team-A-Up-and-running-again.md\n",
      "32 2018-12-02-LOLA-vgs.md\n",
      "33 2019-01-23-Portal-Jam-Oslo-Side.md\n",
      "34 2019-01-23-The-Sounds-We-Like.md\n",
      "35 2019-01-30-portalvideotalkstreaming.md\n",
      "36 2019-02-10-Documentation-and-recommendations-from-the-latest-Portal-Jam.md\n",
      "37 2019-02-10-MonoSynth.md\n",
      "38 2019-02-10-The-Wavesynth.md\n",
      "39 2019-02-11-Alien Reese.md\n",
      "40 2019-02-11-Catch-the-sound–First week's-dive-into-web-audio-programming.md\n",
      "41 2019-02-11-FreakShow.md\n",
      "42 2019-02-11-Giant-Player.md\n",
      "43 2019-02-11-Odyssey.md\n",
      "44 2019-02-11-SineWave-Pad.md\n",
      "45 2019-02-11-SpaghettiPlayer.md\n",
      "46 2019-02-11-The-Pointilator.md\n",
      "47 2019-02-19-Convolverizer.md\n",
      "48 2019-02-22-The-Magic-Piano.md\n",
      "49 2019-02-22-TouchTheAlien.md\n",
      "50 2019-02-27-Ambisonics.md\n",
      "51 2019-03-20-PortalWorkshop.md\n",
      "52 2019-03-26-Øyvind.md\n",
      "53 2019-04-01-MoCap-Recap-My-two-weeks-recap-of-a-Motion-Tracking-workshop.md\n",
      "54 2019-04-03-Advanced-collaborative-spaces-and-tele-immersive-systems.md\n",
      "55 2019-04-03-Daniel-Formo.md\n",
      "56 2019-04-05-Thomas-Hermann.md\n",
      "57 2019-04-07-Pamela-Z.md\n",
      "58 2019-04-12-Ole Nieling Portal Lecture.md\n",
      "59 2019-04-14-Edo-Fouilloux.md\n",
      "60 2019-04-18-brexisonification.md\n",
      "61 2019-04-18-NEO.md\n",
      "62 2019-04-19-The_Sound_of_Traffic.md\n",
      "63 2019-04-24-augmented-reality.md\n",
      "64 2019-04-24-How-music-related-motion-tracking-can-sound.md\n",
      "65 2019-04-30-Sound-In-Space-Exhibition-A-Generic-Overview.md\n",
      "66 2019-05-12-Our-Portal-Train.md\n",
      "67 2019-05-17-Blue-sky-visions-on-sonic-futures.md\n",
      "68 2019-05-19-Experiencing-Ambisonics-Binaurally .md\n",
      "69 2019-06-26-Seismerssion.md\n",
      "70 2019-08-24-Gaute-testpost.md\n",
      "71 2019-08-28-b-team.md\n",
      "72 2019-08-28-Diversity.md\n",
      "73 2019-08-28-Group-C.md\n",
      "74 2019-08-30-Group-A.md\n",
      "75 2019-09-08-Automix.md\n",
      "76 2019-09-14-ims-microture.md\n",
      "77 2019-09-14-ML-Classify-instr.md\n",
      "78 2019-09-14-ML-SH.md\n",
      "79 2019-09-16-Could DSP it.md\n",
      "80 2019-09-16-ML-Drum-Beat-Generation.md\n",
      "81 2019-09-16-ML-Dry-Wet-Sound-Classification.md\n",
      "82 2019-09-16-ML-Roman.md\n",
      "83 2019-09-17-Clustering-high-dimensional-data.md\n",
      "84 2019-09-20-All-about-the-data.md\n",
      "85 2019-09-22-UltraGrid.md\n",
      "86 2019-09-27-ImportanceOfSoundQuality.md\n",
      "87 2019-10-07-Entrepreneurship-Group-B.md\n",
      "88 2019-10-08-Recovio.md\n",
      "89 2019-10-10-Portal-Flowchart.md\n",
      "90 2019-10-12-HySax.md\n",
      "91 2019-10-13-LoopsOnFoam.md\n",
      "92 2019-10-14-AlienHamsterBall.md\n",
      "93 2019-10-14-AudioBend.md\n",
      "94 2019-10-15-b-team-physical-comp-day-1.md\n",
      "95 2019-10-15-Group-C-Physical-Computing.md\n",
      "96 2019-10-15-Physical-Computing-Day1-Group-B.md\n",
      "97 2019-10-16-BodyDrums.md\n",
      "98 2019-10-17-Instant-Music-Subtlety-later.md\n",
      "99 2019-10-18-The-Fonvind-Device.md\n",
      "100 2019-10-18-Wizard_of_Vox.md\n",
      "101 2019-10-20-b-team-sound-light.md\n",
      "102 2019-10-21-many-microphones.md\n",
      "103 2019-10-22-GroupC-Physical-Computing-Day4.md\n",
      "104 2019-10-24-Orchestrash.md\n",
      "105 2019-11-03-Spatial-Trip.md\n",
      "106 2019-11-05-Lilleby.md\n",
      "107 2019-11-25-b-team-deep.md\n",
      "108 2019-11-25-Machine-Learning-Group-C.md\n",
      "109 2019-11-25-ML-GroupA.md\n",
      "110 2019-11-30-ChristmasConert_Trondheim.md\n",
      "111 2019-12-01-b-teams-thoughts.md\n",
      "112 2019-12-02-portal-ideas.md\n",
      "113 2019-12-11-Picture-Carmen.md\n",
      "114 2019-12-15-Trees-As-Speakers.md\n",
      "115 2019-12-15-Trippi-BySykkel-Sounds.md\n",
      "116 2019-12-27-Prototyping-Musical-Instruments.md\n",
      "117 2019-12-30-Staggering-Towards-The-Light.md\n",
      "118 2020-01-27-testing-latency-portal.md\n",
      "119 2020-02-03-Camera-Testing.md\n",
      "120 2020-02-08-The-MIDI-Remixer.md\n",
      "121 2020-02-09-The-Delay-Harmonizer.md\n",
      "122 2020-02-10-osc-guitar.md\n",
      "123 2020-02-10-Sampler.md\n",
      "124 2020-02-11-plant-interface.md\n",
      "125 2020-02-11-Practice-Toolbox-for-guitar.md\n",
      "126 2020-02-12-Making-Noises.md\n",
      "127 2020-02-12-The-Loop-Station.md\n",
      "128 2020-02-25-immersive-portal.md\n",
      "129 2020-03-09-Sonifying-Corona.md\n",
      "130 2020-03-09-Soniweb.md\n",
      "131 2020-03-09-weather-music.md\n",
      "132 2020-03-13-mct-vs-corona.md\n",
      "133 2020-04-01-jacktrip.md\n",
      "134 2020-04-04-CB-Radio-Monitoring.md\n",
      "135 2020-04-05-SoundJack.md\n",
      "136 2020-04-06-Zoom-Jamulus.md\n",
      "137 2020-04-13-soniweb2.md\n",
      "138 2020-04-15-On-communication-in-distributed-environments.md\n",
      "139 2020-04-17-NINJAM.md\n",
      "140 2020-05-02-MotionComposer.md\n",
      "141 2020-05-03-dream-nest.md\n",
      "142 2020-05-04-oceans.md\n",
      "143 2020-05-13-Sonification-of-Standstill.md\n",
      "144 2020-05-15-sound-painting.md\n",
      "145 2020-05-18-streaming.md\n",
      "146 2020-05-20-breathing-through-max.md\n",
      "147 2020-05-21-tsne-adventure.md\n",
      "148 2020-06-10-SHthesis.md\n",
      "149 2020-06-13-MSM-Varpe.md\n",
      "150 2020-06-15-Motivatio-Elias_Sukken_Andersen.md\n",
      "151 2020-06-23-harmonic-interaction-Guy_Sion.md\n",
      "152 2020-07-01-GenderHCI-For-Music.md\n",
      "153 2020-07-31-Gyroshuffle.md\n",
      "154 2020-08-21-B-Team.md\n",
      "155 2020-08-21-Group-A-Yas-Queen.md\n",
      "156 2020-08-21-group-c.md\n",
      "157 2020-08-31-audio-networking-presentations.md\n",
      "158 2020-09-03-jamulus-or-jamu-less.md\n",
      "159 2020-09-06-jamulus-team-a.md\n",
      "160 2020-09-07-jamulus-team-c.md\n",
      "161 2020-09-18-aleksati-music-recognition.md\n",
      "162 2020-09-20-beneficial-unintelligence.md\n",
      "163 2020-09-20-classifying-urban-sounds.md\n",
      "164 2020-09-20-Music-Mood-Classifier.md\n",
      "165 2020-09-20-Support_Vector_Machine.md\n",
      "166 2020-09-20-thomas-ML.md\n",
      "167 2020-09-20-Valley-Of-Despair.md\n",
      "168 2020-09-21-Gaute-sv-Skynet.md\n",
      "169 2020-09-21-MLPclassification.md\n",
      "170 2020-09-21-reinforcement-learning-drum-sequences.md\n",
      "171 2020-09-21-scale-over-chord-ann.md\n",
      "172 2020-09-25-motion-tracking-workshop.md\n",
      "173 2020-09-26-alena-pedal-experience-team-A.md\n",
      "174 2020-09-28-henrikhs-teamB4021ZoomHifiStereo.md\n",
      "175 2020-09-28-the-joys-of-jitsi-team-c.md\n",
      "176 2020-10-12-telematic-musicking-presentations.md\n",
      "177 2020-10-14-alena-programming-team-a.md\n",
      "178 2020-10-14-henrikhs-etude-de-telephone-portable.md\n",
      "179 2020-10-15-henrikhs-teamb-scientific-midterm.md\n",
      "180 2020-10-15-leigh-guitar-vst-interactions.md\n",
      "181 2020-10-16-alena-oscHook-oscRouter.md\n",
      "182 2020-10-16-chaotic-cosmos.md\n",
      "183 2020-10-16-joni-creepy-voice-in-zoom.md\n",
      "184 2020-10-16-musings-bela.md\n",
      "185 2020-10-16-pedropl-pyco-unexpected-spaceship.md\n",
      "186 2020-10-16-simonrs-the-dolphin-drum.md\n",
      "187 2020-10-16-stephedg-phys-comp-project.md\n",
      "188 2020-10-16-stephedg-teamc-scientific-midterm.md\n",
      "189 2020-10-16-sweaty-music.md\n",
      "190 2020-10-16-wenbo-live-mixer.md\n",
      "191 2020-10-17-aleksati-samtar.md\n",
      "192 2020-10-18-abhishec-Musicpathy.md\n",
      "193 2020-10-18-Plunged-Into-Chaos.md\n",
      "194 2020-10-18-singing-shelf-bracket.md\n",
      "195 2020-10-18-ulrikah-the-choir.md\n",
      "196 2020-10-19-thomasanda-HyperGuitar.md\n",
      "197 2020-10-20-rayaml-breath-and-scream.md\n",
      "198 2020-10-20-the-ring-synth.md\n",
      "199 2020-11-01-henrikhs-shaaape.md\n",
      "200 2020-11-03-kovid-keyboard.md\n",
      "201 2020-11-03-pedropl-caminolamentos.md\n",
      "202 2020-11-05-alenacl-beethoven-under-moonlight.md\n",
      "203 2020-11-05-joni-DA-orangecabbage.md\n",
      "204 2020-11-05-stephedg-dafx.md\n",
      "205 2020-11-06-abhishec-butter.md\n",
      "206 2020-11-06-anderlid-SYNTHa.md\n",
      "207 2020-11-06-lindsay-spatialsynth.md\n",
      "208 2020-11-06-short-williakm-vst.md\n",
      "209 2020-11-06-wenbo-fake-piano.md\n",
      "210 2020-11-10-henrikhs-teamb-zoom-ambisonics.md\n",
      "211 2020-11-15-team-c-ambisonic.md\n",
      "212 2020-11-16-spatial-chaos.md\n",
      "213 2020-11-16-team-a-zoom-ambisonics.md\n",
      "214 2020-11-16-team-trondheim-spatial-audio.md\n",
      "215 2020-11-17-how-to-save-the-world.md\n",
      "216 2020-12-02-leigh-slicing-and-dicing.md\n",
      "217 2020-12-03-joni-dark-tetris.md\n",
      "218 2020-12-03-pedropl-multichannel-segmentation-sequencing.md\n",
      "219 2020-12-04-anderlid-graphic-no-7.md\n",
      "220 2020-12-04-stephedg-my-bloody-zerox.md\n",
      "221 2020-12-04-williakm-merging-ockeghem-and-shostakovich.md\n",
      "222 2020-12-05-abhishec-slopy-chops.md\n",
      "223 2020-12-05-alena-chopping-chopin_and-kapustin.md\n",
      "224 2020-12-05-henrikhs-merrySlicemas.md\n",
      "225 2020-12-05-wenbo-slices.md\n",
      "226 2020-12-08-lindsay-strange-fragmenty-song.md\n",
      "227 2020-12-10-aleksati-acx-music-applied.md\n",
      "228 2020-12-10-pedal-applied.md\n",
      "229 2020-12-10-sound-design-for-imtel.md\n",
      "230 2020-12-15-networkedmodular.md\n",
      "231 2021-02-02-henrikhs-clubhouse.md\n",
      "232 2021-02-17-alenacl-funky-balls.md\n",
      "233 2021-02-18-abhishec-Trinity.md\n",
      "234 2021-02-18-henrikhs-DT20.md\n",
      "235 2021-02-18-joni-stress-less.md\n",
      "236 2021-02-28-anderlid-mars1.md\n",
      "237 2021-03-01-joni-teamcjam.md\n",
      "238 2021-03-02-henrikhs-first-weeks.md\n",
      "239 2021-03-05-abhishec-tele-a-jamming.md\n",
      "240 2021-03-25-henrikhs-growing-monoliths-discovered-on-mars.md\n",
      "241 2021-03-26-abhishec-aurora-sonified.md\n",
      "242 2021-03-26-anderlid-sonification.md\n",
      "243 2021-04-16-henrikhs-postureGuard.md\n",
      "244 2021-04-17-stephedg-airguitar.md\n",
      "245 2021-04-18-alenacl-shimsham-mocap.md\n",
      "246 2021-04-18-anderlid-Wet-Dry.md\n",
      "247 2021-04-18-pedropl-airIntruments.md\n",
      "248 2021-04-18-thomasanda-kodalyeartrainer.md\n",
      "249 2021-04-18-wenbo-motiontracking.md\n",
      "250 2021-04-21-abhishec-walking-in-seasons.md\n",
      "251 2021-05-01-pedropl-concert-audio-setup.md\n",
      "252 2021-05-01-pedropl-team-b-concert.md\n",
      "253 2021-05-03-leigh-cross-the-streams.md\n",
      "254 2021-05-03-williakm-dueling-eqs.md\n",
      "255 2021-05-04-alenacl-a-noob-in-the-mct-portal.md\n",
      "256 2021-05-04-wenbo-portalreflection.md\n",
      "257 2021-05-04-wenbo-secondreh.md\n",
      "258 2021-05-05-abhishec-preparing-NTNU-portal.md\n",
      "259 2021-05-05-alenacl-concert-preparations-ensemble-a.md\n",
      "260 2021-05-10-anderlid-applied-dungeon.md\n",
      "261 2021-05-10-stephedg-ritmo-conference.md\n",
      "262 2021-05-10-williakm-hearing-nomono.md\n",
      "263 2021-05-14-aleksati-hardanger-fiddle-performance-analysis.md\n",
      "264 2021-05-14-jacksong-meet-in-space.md\n",
      "265 2021-05-15-paulgk-portable-portal.md\n",
      "266 2021-05-15-ulrikah-rave.md\n",
      "267 2021-06-03-theory-controller.md\n",
      "268 2021-06-20-simonrs-gestures.md\n",
      "269 2021-06-30-ImSoTra.md\n",
      "270 2021-08-25-introduction-team-a.md\n",
      "271 2021-08-27-introduction-team-b.md\n",
      "272 2021-09-17-stephedg-ml.md\n",
      "273 2021-09-18-pedropl-ml-doa-estimation.md\n",
      "274 2021-09-19-wenbo-ml-nl.md\n",
      "275 2021-09-20-abhishec-audio-effect-classification-ml.md\n",
      "276 2021-09-20-alena-genre-classification-ml.md\n",
      "277 2021-09-20-joachipo-internet-time-delay.md\n",
      "278 2021-09-22-arvidf-fight-latency-with-latency.md\n",
      "279 2021-09-22-hughav-ant-topology.md\n",
      "280 2021-09-22-josephcl-algorithmic-interdependence.md\n",
      "281 2021-09-22-olivegr-telematic-reality-check.md\n",
      "282 2021-09-22-sofia-telematic-performance-and-communication.md\n",
      "283 2021-09-23-jakobh-embodiness-in-vr.md\n",
      "284 2021-09-26-kriswent-network-improvisation.md\n",
      "285 2021-10-25-jakobhoy-TeamB-DontStopTheMusic.md\n",
      "286 2021-10-25-team-a-python-3.md\n",
      "287 2021-10-30-pedropl-ethrio.md\n",
      "288 2021-11-01-lindsay-D'n'B-ims.md\n",
      "289 2021-11-01-sofiagon-soundation-review.md\n",
      "290 2021-11-02-abhishec-cirquencer-ims.md\n",
      "291 2021-11-02-joachipo-earsketch.md\n",
      "292 2021-11-03-jakobhoy-SatelliteSession.md\n",
      "293 2021-11-03-josephcl-skytracks-review.md\n",
      "294 2021-11-03-stephedg-notestack.md\n",
      "295 2021-11-04-halvorsh-triadmin.md\n",
      "296 2021-11-14-stephedg-feedback.md\n",
      "297 2021-11-14-telematic-conducting.md\n",
      "298 2021-11-15-abhishec-audio-latency-telematic-context.md\n",
      "299 2021-11-15-alenacl-video-latency-clarifications.md\n",
      "300 2021-11-15-anderlid-portal.md\n",
      "301 2021-11-15-joni-touchpoint-audio-latency.md\n",
      "302 2021-11-15-lindsay-room-modes.md\n",
      "303 2021-11-15-pedropl-avsync-streaming.md\n",
      "304 2021-11-15-wenboyi-audiolatencyy.md\n",
      "305 2021-11-26-jarlefst-portal-feedback.md\n",
      "306 2021-11-26-josephcl-chorale-rearranger.md\n",
      "307 2021-11-27-arvidf-mixing-in-python.md\n",
      "308 2021-12-10-arvidf-granjular-concert.md\n",
      "309 2021-12-10-joachipo-telematic.md\n",
      "310 2021-12-10-pedropl-chasing-stars.md\n",
      "311 2021-12-14-fractionMari-micro-and-macro.md\n",
      "312 2021-12-20-alenacl-rockheim.md\n",
      "313 2022-02-18-joachipo-measuring-latency.md\n",
      "314 2022-02-21-josephcl-dfa-laa-latency-approaches.md\n",
      "315 2022-02-23-jakobhoydal_posturiser.md\n",
      "316 2022-03-22-josephcl-osc-sonic-pi.md\n",
      "317 2022-04-02-aleksati-5g-nmp.md\n",
      "318 2022-04-24-hughav-ambisonics-mix.md\n",
      "319 2022-04-24-kriswent-using-udp-and-osc-for-nmp.md\n",
      "320 2022-04-24-sofiagon-hybrid-learning-environments.md\n",
      "321 2022-04-25-joachipo-network-performance.md\n",
      "322 2022-05-12-jonimok-crossmodal-correspondences.md\n",
      "323 2022-05-15-pedropl-human-machine-impro.md\n",
      "324 2022-05-15-rayam-visualizing-psychophysiological-responses-to-music-stimuli.md\n",
      "325 2022-05-15-stephedg-algorithmic-composition-explorer.md\n",
      "326 2022-05-19-jakobhoydal-Sitting_vs_standing.md\n",
      "327 2022-05-19-sofiagon-what-is-a-gesture.md\n",
      "328 2022-05-20-arvidf-ml-guitareffect-modeling.md\n",
      "329 2022-05-20-hughav-mo-cap-reconfigurations.md\n",
      "330 2022-05-20-hughav-VAE-sampler.md\n",
      "331 2022-05-20-jakobhoydal-Reverb_classification.md\n",
      "332 2022-05-20-joachipo-estimating-repertoire-size.md\n",
      "333 2022-05-20-josephcl-air-drumming.md\n",
      "334 2022-05-20-josephcl-recognizing-and-predicting-groove.md\n",
      "335 2022-05-20-kriswent-extending-the-keyboard-through-motion-capture-and-modular-synthesis.md\n",
      "336 2022-05-20-kriswent-generating-piano-accompaniments-using-fourier-transforms.md\n",
      "337 2022-05-20-wenbo-when-hearts-beat-as-one-thesis.md\n",
      "338 2022-05-22-hughav-telematic-concert-portal.md\n",
      "339 2022-05-27-joachipo-telematic-concert.md\n",
      "340 2022-05-30-alena-exploration-of-emotional-response-to-vibrotactile-music.md\n",
      "341 2022-06-02-abhishec-thesis-unsupervised-classification.md\n",
      "342 2022-06-02-lindsay-robocapo.md\n",
      "343 2022-07-05-joachipo-telenor-concert.md\n",
      "344 2022-08-23-kristeic_team_blog_post.md\n",
      "345 2022-08-26-ahmetem-team-b.md\n",
      "346 2022-08-26-aysimab-hello-team-c.md\n",
      "347 2022-10-03-the-impact-and-importance-of-network-based-musical-collaboration-in-the-post-covid-world.md\n",
      "348 2022-10-21-alexanj-euclidean-sequencers-pd.md\n",
      "349 2022-11-01-fabianst-no-input-mixing-pd.md\n",
      "350 2022-11-10-henrikhs-FAM-synth.md\n",
      "351 2022-11-11-josephcl-contact-mic-looper.md\n",
      "352 2022-11-13-jakobhoydal-draapen.md\n",
      "353 2022-11-21-fabianst-telematic-rehersal.md\n",
      "354 2022-11-23-josephcl-popsenteret.md\n",
      "355 2022-11-24-alexanjw-audio-engineering-nmps.md\n",
      "356 2022-11-26-zeusaphone.md\n",
      "357 2022-11-27-jackeh-performing-recording-jacktrip.md\n",
      "358 2022-11-27-joachipo-telenor-5g-report.md\n",
      "359 2022-11-27-kristeic-audio-routing-101.md\n",
      "360 2022-11-28-aysimab-music-mode.md\n",
      "361 2022-11-28-aysimab-telematic-experience-performers.md\n",
      "362 2022-11-28-fabianst-performer-blog-post.md\n",
      "363 2022-11-28-jackeh-kristeic-optic-fibre-ensemble-technical.md\n",
      "364 2022-11-29-masoudn-blackhole.md\n",
      "365 2022-11-30-arvidf-transformer1.md\n",
      "366 2022-12-03-olivegr-musetech.md\n",
      "367 2022-12-03-olivegr-yggdrasil.md\n",
      "368 2022-12-04-ahmetem-livestreaming-telematic.md\n",
      "369 2022-12-04-ahmetem-making-a-telematic-concert-happen.md\n",
      "370 2022-12-06-ninojak-Jacktrip-Vs-Sonobus.md\n"
     ]
    }
   ],
   "source": [
    "path = './posts'\n",
    "files = os.listdir(path)\n",
    "\n",
    "# iterate files, get text, remove frontmatter\n",
    "all = ''\n",
    "for i, file in enumerate([s for s in files if s.endswith('.md')]):\n",
    "    text = ''\n",
    "    with open(os.path.join(path, file), encoding = 'utf-8') as f:\n",
    "        md = f.read()\n",
    "        md = re.sub(\"\\n\", \" \", md)\n",
    "        md = re.sub(r\"---.*---\", '', md) # frontmatter\n",
    "        text = md_to_text(md)\n",
    "        all += text\n",
    "\n",
    "# clean text\n",
    "cleaned = clean_text(all)\n",
    "\n",
    "# write all to txt\n",
    "with open('all_words.txt', 'w', encoding='utf-8') as f:\n",
    "    f.write(cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f88ea948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2233883"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b1b2c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>words</th>\n",
       "      <th>freq</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>the</td>\n",
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       "      <th>1</th>\n",
       "      <td>to</td>\n",
       "      <td>8371</td>\n",
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       "      <th>2</th>\n",
       "      <td>and</td>\n",
       "      <td>8002</td>\n",
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       "      <th>3</th>\n",
       "      <td>of</td>\n",
       "      <td>6977</td>\n",
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       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>6657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>15040</th>\n",
       "      <td>nontechnical</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15041</th>\n",
       "      <td>disk</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15042</th>\n",
       "      <td>clientserver</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15043</th>\n",
       "      <td>kilometres</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15044</th>\n",
       "      <td>lowlatencymilliseconds</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15045 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        words   freq\n",
       "0                         the  19044\n",
       "1                          to   8371\n",
       "2                         and   8002\n",
       "3                          of   6977\n",
       "4                           a   6657\n",
       "...                       ...    ...\n",
       "15040            nontechnical      1\n",
       "15041                    disk      1\n",
       "15042            clientserver      1\n",
       "15043              kilometres      1\n",
       "15044  lowlatencymilliseconds      1\n",
       "\n",
       "[15045 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = cleaned.split(' ')\n",
    "counts = Counter(words)\n",
    "commons = counts.most_common()\n",
    "\n",
    "df_freq = pd.DataFrame(commons, columns=['words','freq'])\n",
    "\n",
    "df_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fba332ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>words</th>\n",
       "      <th>freq</th>\n",
       "      <th>w_len</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>345.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>name</td>\n",
       "      <td>97.5</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>made</td>\n",
       "      <td>630.0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>we</td>\n",
       "      <td>6642.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>are</td>\n",
       "      <td>3182.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265731</th>\n",
       "      <td>comfortable</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265733</th>\n",
       "      <td>running</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265736</th>\n",
       "      <td>links</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265739</th>\n",
       "      <td>jacktrip</td>\n",
       "      <td>175.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265742</th>\n",
       "      <td>jacktrip</td>\n",
       "      <td>175.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              words    freq  w_len\n",
       "1              team   345.0      4\n",
       "2              name    97.5      4\n",
       "3              made   630.0      4\n",
       "4                we  6642.5      2\n",
       "5               are  3182.5      3\n",
       "...             ...     ...    ...\n",
       "265731  comfortable    40.0     11\n",
       "265733      running   190.0      7\n",
       "265736        links    40.0      5\n",
       "265739     jacktrip   175.0      8\n",
       "265742     jacktrip   175.0      8\n",
       "\n",
       "[169480 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_words = pd.DataFrame(words, columns = ['words'])\n",
    "\n",
    "df = df_words.merge(df_freq, how='left')\n",
    "\n",
    "# unwanted words\n",
    "unwanted_words = ['', 'the', 'to', 'and', 'of', 'a', 'in', 'is', 'for', 'with', 'that']\n",
    "df = df[~df['words'].isin(unwanted_words)]\n",
    "\n",
    "# filter less freq (<10)\n",
    "df = df[df['freq']>10]\n",
    "\n",
    "\n",
    "# scale frequency\n",
    "df['freq'] = df['freq']*2.5\n",
    "\n",
    "# create \n",
    "df['w_len'] = df['words'].str.len()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e16a21dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>words</th>\n",
       "      <th>freq</th>\n",
       "      <th>w_len</th>\n",
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       "    <tr>\n",
       "      <th>73879</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>6642.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17300</th>\n",
       "      <td>we</td>\n",
       "      <td>6642.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55139</th>\n",
       "      <td>we</td>\n",
       "      <td>6642.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6262</th>\n",
       "      <td>we</td>\n",
       "      <td>6642.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      words    freq  w_len\n",
       "73879    we  6642.5      2\n",
       "55028    we  6642.5      2\n",
       "17300    we  6642.5      2\n",
       "55139    we  6642.5      2\n",
       "6262     we  6642.5      2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='freq', ascending=False).head()#to_csv('most_common_sorted_words.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7073f927",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_mean</th>\n",
       "      <th>w_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1108.7</td>\n",
       "      <td>6.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1041.2</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1148.9</td>\n",
       "      <td>6.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1035.7</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>420.9</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6774</th>\n",
       "      <td>1525.5</td>\n",
       "      <td>5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6775</th>\n",
       "      <td>1114.9</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6776</th>\n",
       "      <td>1213.2</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6777</th>\n",
       "      <td>1030.6</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6778</th>\n",
       "      <td>1276.0</td>\n",
       "      <td>6.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6779 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      f_mean  w_len\n",
       "0     1108.7   6.36\n",
       "1     1041.2   6.08\n",
       "2     1148.9   6.16\n",
       "3     1035.7   6.08\n",
       "4      420.9   5.40\n",
       "...      ...    ...\n",
       "6774  1525.5   5.12\n",
       "6775  1114.9   5.76\n",
       "6776  1213.2   4.72\n",
       "6777  1030.6   5.84\n",
       "6778  1276.0   6.28\n",
       "\n",
       "[6779 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create averaged dataframe\n",
    "df_avg = pd.DataFrame()\n",
    "roll = 25\n",
    "step = 25\n",
    "df_avg['f_mean'] = df['freq'].rolling(roll, step=step).mean().dropna().reset_index(drop=True)\n",
    "df_avg['w_len'] = df['w_len'].rolling(roll, step=step).mean().dropna().reset_index(drop=True)\n",
    "\n",
    "df_avg #, df_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18a408eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_mean</th>\n",
       "      <th>w_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6779.000000</td>\n",
       "      <td>6779.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1174.071840</td>\n",
       "      <td>5.271999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>349.727644</td>\n",
       "      <td>0.684012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>177.500000</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>927.600000</td>\n",
       "      <td>4.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1156.400000</td>\n",
       "      <td>5.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1404.550000</td>\n",
       "      <td>5.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2493.400000</td>\n",
       "      <td>8.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            f_mean        w_len\n",
       "count  6779.000000  6779.000000\n",
       "mean   1174.071840     5.271999\n",
       "std     349.727644     0.684012\n",
       "min     177.500000     2.800000\n",
       "25%     927.600000     4.800000\n",
       "50%    1156.400000     5.240000\n",
       "75%    1404.550000     5.720000\n",
       "max    2493.400000     8.200000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_avg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e55b208e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  2.33168085e-08,  1.84573464e-07, ...,\n",
       "       -1.21225211e-07, -5.79717102e-08, -0.00000000e+00])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sonify\n",
    "\n",
    "sr = 48000\n",
    "sr_ms = int(sr/1000)\n",
    "signal = np.array([])\n",
    "for i, row in df_avg.iterrows():\n",
    "    # for every event create audio signal and append it to the main signal\n",
    "    freq = row['f_mean']\n",
    "    note_dur = int(row['w_len']**2.5*1.5+10) #ms\n",
    "    start_sample = i*note_dur*sr_ms\n",
    "    dur_sample = note_dur*sr_ms\n",
    "\n",
    "    # sin wave\n",
    "    f = freq\n",
    "    A = 1 #vel/max_vel\n",
    "    t = np.arange(0,note_dur/1000,1/sr)\n",
    "    s = A*np.sin(2*np.pi*f*t)\n",
    "\n",
    "    # window\n",
    "    window = scipy.signal.windows.general_hamming(len(s), alpha=0.5)\n",
    "\n",
    "    # append\n",
    "    signal = np.concatenate((signal, s*window))\n",
    "\n",
    "for i in df_avg.index:\n",
    "    start_sample = i*note_dur*sr_ms\n",
    "    dur_sample = note_dur*sr_ms\n",
    "\n",
    "signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc90f543",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export\n",
    "\n",
    "sf.write('mct_blog_sonified_testing_01.wav', signal, sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41fb10f1",
   "metadata": {},
   "source": [
    "# notes below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "41ac9081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.03533556,  0.07062699, ..., -0.10192446,\n",
       "       -0.06801529, -0.03402735])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# equal note durations (not used)\n",
    "sr = 48000\n",
    "sr_ms = int(sr/1000)\n",
    "note_dur = 100 #ms\n",
    "dat = avg\n",
    "signal = np.zeros(len(dat)*note_dur*sr_ms)\n",
    "for i, v in enumerate(dat):\n",
    "    # for every event create audio signal and embed it into the main signal\n",
    "    freq = v\n",
    "    start_sample = i*note_dur*sr_ms\n",
    "    dur_sample = note_dur*sr_ms\n",
    "\n",
    "    # sin wave\n",
    "    f = freq\n",
    "    A = 1 #vel/max_vel\n",
    "    t = np.arange(0,note_dur/1000,1/sr)\n",
    "    s = A*np.sin(2*np.pi*f*t)\n",
    "\n",
    "    # place it into its position in the main signal by addition\n",
    "    signal[start_sample : start_sample + dur_sample] += s\n",
    "    anan = signal[0 : start_sample + dur_sample]\n",
    "    \n",
    "signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "2edeb61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11451d896c0>]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# chirp (not used)\n",
    "# chirp\n",
    "chirp = np.array([])\n",
    "if i != 0:\n",
    "    interval_length = 50/1000 # in seconds\n",
    "    fs = sr # sampling of your signal\n",
    "    f0 = df_avg['f_mean'].iloc[i-1] # frequency 1\n",
    "    f1 = freq  # frequency 2\n",
    "    t = np.linspace(0, interval_length, int(fs * interval_length))\n",
    "    chirp = scipy.signal.chirp(t, f0=f0, f1=f1, t1=interval_length, method='linear')\n",
    "\n",
    "# chirp window\n",
    "window_chirp = scipy.signal.windows.general_hamming(len(chirp), alpha=0.8)\n",
    "\n",
    "signal[start_sample-:start_sample+25] += chirp*window_chirp\n",
    "\n",
    "# derivative (not used)\n",
    "df_diff = df.copy()\n",
    "df_diff['freq'] = df['freq'].diff()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7 (tags/v3.10.7:6cc6b13, Sep  5 2022, 14:08:36) [MSC v.1933 64 bit (AMD64)]"
  },
  "vscode": {
   "interpreter": {
    "hash": "f3794dd520038bf32c1a0319f41251441e251633eac38062c0ed6283041b9d16"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}